Choice-based learning : lecture-based or team learning?

This study investigates choice-based learning as a choice between lecture-based or team learning in a large class at a large university in a European country. The study was designed as a between-subjects quasi-experiment where students were allocated their preferred learning approach. Data were collected for eight consecutive years (2008-2016). Based on quantitative and qualitative data, this study investigates the effect of choice-based learning on choice satisfaction, student selection and on student learning outcomes. The results show that team learning has a positive effect on learning outcomes. If students are faced with the choice, the majority select lecture-based learning. Additionally, both student groups are satisfied with their selected learning paths but selected them for specific reasons. Finally, choice-based learning provides job satisfaction for the instructors of both learning paths. These results can re-energize the ongoing discussion on why and how to engage students in learning activities

Is Rewarding Beneficial to Behavior?

What effect does a reward system have on ninth grade student behavior? Is there a way to have my students behave better than they currently are? There is plenty of research that has been conducted on different types of distracting behavior as well as different types of systems and programs that try to influence that behavior. A ninth grade class has been selected, observed, and data has been recorded on any disruptive or unwanted behavior for three weeks. The first three weeks the student had no clue they were being checked for behavior. For the second three weeks, a reward system was implanted for the class. The class was given the opportunity to earn a “free day” in physical education class by decreasing their disruptive behavior during class. In this study the students proved that if given the chance to work towards something they would work harder and behave better

Using Gameplay Patterns to Gamify Learning Experiences

Gamification refers to the use of gaming elements to enhance user experience and engagement in non-gaming systems. In this paper we report the design and implementation of two higher education courses in which ludic elements were used to enhance the quality of the learning experience. A game can be regarded as a system of organised gameplay activities, and a course can be regarded as a system of organised learning activities. Leveraging this analogy, analysing games can provide valuable insights to organise learning activities within a learning experience. We examined a sample of successful commercial games to identify patterns of organisation of gameplay activities that could be applied to a course design. Five patterns were identified: quest structure, strategic open-endedness, non-linear progression, orientation, and challenge-based reward. These patterns were then used to define the instructional design of the courses. As a result, courses were organised as systems of quests that could be tackled through different strategies and in a non-linear way. Students received frequent feedback and were rewarded according to the challenges chosen, based on mechanics common in quest-based games. The courses involved two lecturers and 70 students. Learning journals were used throughout the term to collect data regarding student perceptions on the clarity and usefulness of the gamified approach, level of motivation and engagement in the courses, and relevance of the activities proposed. Results show that students felt challenged by the activities proposed and motivated to complete them, despite considering most activities as difficult. Students adopted different cognitive and behavioural strategies to cope with the courses’ demands. They had to define their own team project, defining the objectives, managing their times and coordinating task completion. The regular and frequent provision of feedback was highly appreciated. A sense of mastery was promoted and final achievement was positively impacted by the gamified strategy

How Do Shocks to Non-Cognitive Skills Affect Test Scores?

This paper investigates the extent to which test performance is affected by shocks to non-cognitive skills. 440 students took a low stakes mathematics test. About half of them were exposed to positive affirmation while being given test instructions, whereas the other half served as controls. The students were allocated to 14 tutorials and randomisation was conducted at the tutorial level. Mean comparisons suggest that test scores were raised by the intervention. In particular, students with low maths grades and with self-assessed difficulties in maths gained from the positive affirmation. Results suggest that teachers might increase their students' performance by interventions to their non-cognitive skills. Inference is obtained by four different methods that take into account that randomisation was clustered at the tutorial group level. These methods are evaluated in a Monte Carlo study for data generating processes which resemble actual data. We find that randomisation inference followed by the wild cluster bootstrap have superior size properties compared to conventional approaches.test scores, non-cognitive skills, cluster randomised trial, wild cluster bootstrap, randomisation inference

How Do Shocks to Non-Cognitive Skills Affect Test Scores?

This paper investigates the extent to which test performance is affected by shocks to noncognitive skills. 440 students took a low stakes mathematics test. About half of them were exposed to positive affirmation while being given test instructions, whereas the other half served as controls. The students were allocated to 14 tutorials and randomisation was conducted at the tutorial level. Mean comparisons suggest that test scores were raised by the intervention. In particular, students with low maths grades and with self-assessed difficulties in maths gained from the positive affirmation. Results suggest that teachers might increase their students' performance by interventions to their non-cognitive skills. Inference is obtained by four different methods that take into account that randomisation was clustered at the tutorial group level. These methods are evaluated in a Monte Carlo study for data generating processes which resemble actual data. We find that randomisation inference followed by the wild cluster bootstrap have superior size properties compared to conventional approaches.test scores, non-cognitive skills, cluster randomised trial, wild cluster bootstrap, randomisation inference

Designing Engaging Learning Experiences in Programming

In this paper we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies to explore how programming tasks could be framed to motivate learners. Our empirical findings from these four field studies are summarized here, with a particular focus upon one – Whack a Mole – which compared the use of a physical interface with the use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions reported by two sets of participant undergraduate students were analyzed, identifying the links between the emotions experienced during programming and their origin. Evidence was collected of the very positive emotions experienced by learners programming with a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface. A follow-up study provided further evidence of the motivation of personalized design of programming tangible physical artefacts. Collating all the evidence led to the design of a set of ‘Learning Dimensions’ which may provide educators with insights to support key design decisions for the creation of engaging programming learning experiences